Over the past decade, social network analysis has earned pivotal eminence in the area of web mining and information retrieval. Community detection, being the indispensable part of social network analysis; has garnered far reaching usance in business analytics, healthcare, security, research and policy making. With the embodiment of copious domains to social networks and unprecedented rise in the data — produced, accessed and stored globally; the task of handling the unpredictable, dynamic and ever evolving topological nature of social networks has become arduous. In this regard, quantum computing (QC) has emerged as the most promising trailblazer guaranteeing unprecedented data storage and manipulation capabilities by- dynamic allocation of cluster size and architecture, quantum parallelism, reduced parameter dependency, etc. QC based algorithms have registered exponential speedup over many classical problems with better efficiency and abated time complexity; apart from solving NP-hard problems that were unrealizable classically.

Accordingly, a comprehensive literature survey has been presented for social network analysis and community detection highlighting the limitations prevalent in the current technologies. A brief insight into quantum computing and its proficiency in rendering to larger storage systems has been presented; as a solution to the inherent problems present in the existing community detection approaches. A systematic account of quantum computing based community detection techniques has been summarized and discussed as a more prudent future alternative to social network analysis and community detection. Lastly, complexity analysis and modularity based comparison of QC based algorithms with other state of the art algorithms has been carried out to establish the supremacy of quantum algorithms in community detection.

在过去的十年中，社交网络分析在Web挖掘和信息检索领域赢得了举足轻重的地位。社区检测是社交网络分析不可或缺的一部分；在业务分析，医疗保健，安全，研究和政策制定方面获得了广泛的应用。随着社交网络的丰富领域的体现以及全球范围内生成，访问和存储的数据的空前增长；处理社交网络的不可预测，动态和不断发展的拓扑性质的任务变得艰巨。在这方面，量子计算（QC）已成为最有前途的开拓者，它通过动态分配簇的大小和体系结构，量子并行性，减少的参数依赖性等来保证空前的数据存储和处理能力。基于QC的算法在许多经典问题上均实现了指数级加速，效率更高且时间复杂度有所降低。除了解决传统上无法实现的NP难题。

因此，已经提出了用于社会网络分析和社区检测的综合文献调查，突出显示了当前技术中普遍存在的局限性。简要介绍了量子计算及其在大型存储系统中的渲染能力。解决现有社区检测方法中存在的固有问题。总结和讨论了基于量子计算的社区检测技术的系统说明，作为对社交网络分析和社区检测的更审慎的未来替代方案。最后，已经进行了基于QC的算法与其他现有算法的复杂度分析和基于模块性的比较，以建立量子算法在社区检测中的优势。